Shafin Rahman

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 10 papers; trusts 1,284 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 246 citations into it; #4,564 of 21,575 by trust here, #4,887 by citations
• Artificial Intelligence — 211 citations into it; #5,770 of 35,345 by trust here, #6,720 by citations
• Representation & Self-Supervised Learning — 97 citations into it
• Radiology, Nuclear Medicine and Imaging — 63 citations into it; #457 of 3,265 by trust here, #1,169 by citations
• Meta & Continual Learning — 46 citations into it; #319 of 1,041 by trust here, #274 by citations
• Computational Mechanics — 30 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Natural Language Processing — 26 citations into it

Whom Shafin Rahman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Tomas Mikolov
5. Ross B. Girshick
6. Ilya Sutskever
7. Karen Simonyan
8. Diederik P. Kingma
9. Shaoqing Ren
10. Yoshua Bengio

Who trusts Shafin Rahman overall

1. Ali Cheraghian
2. Pengkai Zhu
3. Xiuye Gu
4. Salman Hameed Khan
5. Weicheng Kuo
6. Hanxiao Wang
7. Liunian Harold Li
8. Tsung-Yi Lin
9. Xingyi Zhou
10. Alireza Zareian

Papers from Shafin Rahman's perspective

Ordered by how strongly the researchers Shafin Rahman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts (2018) — 168 citations
• Improved Visual-Semantic Alignment for Zero-Shot Object Detection (2020) — 124 citations
• Transductive Learning for Zero-Shot Object Detection (2019) — 84 citations
• Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (2021) — 233 citations
• Zero-Shot Object Detection: Joint Recognition and Localization of Novel Concepts (2020) — 58 citations
• Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021) — 81 citations
• Transductive Zero-Shot Learning for 3D Point Cloud Classification (2019) — 68 citations
• Any-Shot Object Detection (2020) — 19 citations
• Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects (2019) — 48 citations
• Zero-shot Learning of 3D Point Cloud Objects (2019) — 66 citations